Content

Top Companies of NASDAQ 100 in 2022

Here are the top companies on the NASDAQ 100 index in 2022. NASDAQ 100 is one of the most prominent large-cap growth indices in the world.

Many companies listed in the NASDAQ 100 operate in the tech sector. That is why many investors who are focused investing in tech stocks also invest in NASDAQ index to grow their funds

What is NASDAQ 100?

NASDAQ 100 is a stock market index composed of the 100 largest and most actively traded companies in the United States of America in the non- financial sector and are segmented under technology, retail, industrial, biotechnology, health care, telecom, transportation, media and services sectors.

Acknowledgement

Data collected from Yahoo Finance.

Context

In this dataset you can find the Top 100 companies in the technology sector. You can also find 5 of the most important and used indices in the financial market as well as a list of all the companies in the S&P 500 index and in the technology sector.

The Global Industry Classification Standard also known as GICS is the primary financial industry standard for defining sector classifications. The Global Industry Classification Standard was developed by index providers MSCI and Standard and Poor’s. Its hierarchy begins with 11 sectors which can be further delineated to 24 industry groups, 69 industries, and 158 sub-industries.

You can read the definition of each sector here.

The 11 broad GICS sectors commonly used for sector breakdown reporting include the following: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, Utilities and Real Estate.

In this case we will focuse in the Technology Sector. You can see all the sectors and industry groups here.

To determine which companies, correspond to the technology sector, we use Yahoo Finance, where we rank the companies according to their “Market Cap”. After having the list of the Top 100 best valued companies in the sector, we proceeded to download the historical data of each of the companies using the NASDAQ website.

Regarding to the indices, we searched various sources to find out which were the most used and determined that the 5 most frequently used indices are: Dow Jones Industrial Average (DJI), S&P 500 (SPX), NASDAQ Composite (IXIC), Wilshire 5000 Total Market Inde (W5000) and to specifically view the technology sector SPDR Select Sector Fund - Technology (XLK). Historical data for these indices was also obtained from the NASDQ website.

Content

In total there are 107 files in csv format. They are composed as follows:

• 100 files contain the historical data of tech companies.
• 5 files contain the historical data of the most used indices.
• 1 file contain the list of all the companies in the S&P 500 index.
• 1 file contain the list of all the companies in the technology sector.

Column Description

Every company and index file has the same structure with the same columns:

Date: It is the date on which the prices were recorded. High: Is the highest price at which a stock traded during the course of the trading day. Low: Is the lowest price at which a stock traded during the course of the trading day. Open: Is the price at which a stock started trading when the opening bell rang. Close: Is the last price at which a stock trades during a regular trading session. Volume: Is the number of shares that changed hands during a given day. Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

The two other files have different columns names:

List of S&P 500 companies

Symbol: Ticker symbol of the company. Name: Name of the company. Sector: The sector to which the company belongs.

Technology Sector Companies List

Symbol: Ticker symbol of the company. Name: Name of the company. Price: Current price at which a stock can be purchased or sold. (11/24/20) Change: Net change is the difference between closing prices from one day to the next. % Change: Is the difference between closing prices from one day to the next in percentage. Volume: Is the number of shares that changed hands during a given day. Avg Vol: Is the daily average of the cumulative trading volume during the last three months. Market Cap (Billions): Is the total value of a company’s shares outstanding at a given moment in time. It is calculated by multiplying the number of shares outstanding by the price of a single share. PE Ratio: Is the ratio of a company's share (stock) price to the company's earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued.

Acknowledgements

SEC EDGAR | Company Filings NASDAQ | Historical Quotes Yahoo Finance | Technology Sector Wikipedia | List of S&P 500 companies S&P Dow Jones Indices | S&P 500 [S&P Dow Jones Indices | DJI](https://www.spglobal.com/spdji/en/i...

This statistic gives information on Yahoo!'s net income from 2004 to 2016. In the last reported year, the internet company's GAAP net loss was *** million US dollars, down from a net income of *** billion US dollars in 2014.

Yahoo has had its share of financial troubles, in part due to Google’s almost complete domination of market sectors where Yahoo used to be an important player, such as the search engine market. For example, as of April 2015, just under * percent of worldwide internet users search the web using Yahoo’s service, while more than ** percent use Google Search. But despite its ups and downs, the company has remained one of the most relevant multinational technology companies in the world. In 2014, Yahoo’s net income was a reported *** billion U.S. dollars, up from *** billion in the previous year. That same year, the company’s yearly revenue however was the second-lowest in the past decade – *** billion U.S. dollars. Especially the second quarter of 2014 displays lower than ever revenues for the company, as compared to previous years – just slightly over * billion U.S. dollars. According to the most recent report regarding Yahoo’s quarterly net income, the company generated a **** billion U.S. dollars profit in the third quarter of 2014, as a result the company's sale of Alibaba shares, but also a net loss of ***** million U.S. dollars in the second quarter of 2015. Yahoo was founded in the mid ***** in California, in the midst of the Silicon Valley technological boom. It is mostly known for its search engine, Yahoo Search, and the Yahoo web portal, featuring such services as Yahoo Finance, Yahoo News, Yahoo Answers and most notably Yahoo Mail. The company, which has made a lot of acquisitions since its modest beginnings, also provides advertising services, online mapping and video sharing. Since it acquired Tumblr in 2013, the company has also started to move into the social media sector. As of 2015, Yahoo is the second-most popular website in the United States, after Google, with more than *** million unique visitors per month on all of its properties combined.

Replication files for "Differences from Differencing: Should Local Projections with Observed Shocks be Estimated in Levels or Differences?" Jeremy Piger and Thomas Stockwell, 2025, Journal of Applied Econometrics.

Matlab Programs

There are three Matlab programs in this repository:

LP_MonteCarlo_Final.m - This is the primary program for conducting the simulations reported in the paper. Using this code, one can replicates Figures 1-2, 4-5, and 7-17

LPIV_MonteCarlo_Final.m - This is the program to conduct simulations using LP-IV. Replicates Figure 19

Application_Jarocinski_Karadi_Shock_Final.m - This is the program to produce results for the application. Replicates Figure 20

Data Files

The Matlab programs collectively use three data files, contained in the “Data” folder:

GDPC1.csv - Data file containing U.S. real GDP, 1947:Q1 - 2024:3, FRED code: GDPC1.

CEE_VAR_Data.csv - contains ten quarterly series necessary to construct the nine variables used to estimate the Christiano, Eichenbaum and Evans (2005) 9-variable VAR:

1) GDPC1 = U.S. real GDP, 1947:Q1 - 2024:Q3, FRED code: GDPC1 2) PCEC96 = U.S. real personal consumption expenditures, 1947:Q1 - 2024:Q3, FRED code: PCEC96 3) GPDEF = U.S. GDP implicit price deflator, 1947:Q1 - 2024:Q3, FRED code: GDPDEF 4) GPDIC1 = U.S. real gross private domestic investment, 1947:Q1 - 2024:Q3, FRED code: GPDIC1 5) COMPNFB = Nonfarm business sector: hourly compensation for all workers, 1947:Q1 - 2024:Q3, FRED code: COMPNFB 6) PRS85006023 = Nonfarm business sector: average weekly hours for all workers, 1947:Q1 - 2024:Q3, FRED code: PRS85006023 7) OPHNFB = Nonfarm business sector: labor productivity (output per hour) for all workers, 1947:Q1 - 2024:Q3, FRED code: OPHNFB 8) FEDFUNDS = Federal funds effective rate, 1954:Q3 - 2024:Q3, FRED code: FEDFUNDS 9) CP = Corporate profits after tax, 1947:Q1 - 2024:Q3, FRED code: CP 10) M2SL = M2 monetary aggregate, 1947:Q1 - 2024:Q3, FRED code: M2SL

application_data.csv - contains seven monthly series used for the application:

1) indpro = U.S. industrial production index, Jan. 1989-Sep. 2024, FRED code: INDPRO 2) CPI = U.S. consumer price index, Jan. 1989-Sep. 2024, FRED code: CPIAUCSL 3) 1yrTreasury = Market Yield on U.S. Treasury Securities at 1-Year Constant Maturity, Jan. 1989-Sep. 2024, FRED code: DGS1 4) SP500 = S&P 500 Index, Jan. 1989-Sep. 2024, Yahoo Finance code GSPC and FRED code: SP500 5) EBP = Gilchrist and Zakrajsek (2012, AER) Excess Bond Premium, Jan 1989-Sep.2024, obtained from the dataset provided for Bauer and Swanson (2023, NBER Macro Annual) on Michael Bauer’s website: https://www.michaeldbauer.com/research/. 6) MP_median = Jarocinski and Karadi (2020, AEJ Macro) U.S. monetary policy shock, Feb. 1990-Sep. 2024, obtained from: https://marekjarocinski.github.io/jkshocks/jkshocks.html 7) CBI_median = Jarocinski and Karadi (2020, AEJ Macro) U.S. central bank information shock, Feb. 1990-Sep. 2024, obtained from: https://marekjarocinski.github.io/jkshocks/jkshocks.html

CRB Index rose to 378.33 Index Points on December 1, 2025, up 0.45% from the previous day. Over the past month, CRB Index's price has fallen 0.80%, but it is still 10.95% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on December of 2025.

Current 30 components of the Dow Jones Industrial Average, and their Open, High, Low, Close, Adjusted Close, Volume data since 1980, for stocks that were listed then. Updates daily using the Yahoo Finance API.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

This is a collection of daily (OHLC, Adj Close and Volume) data for the Dow Jones Companies from 1st January 2000 to Dec 6th 2017.

The Dow Jones is an index that shows how 30 large publicly owned companies based in the United States have traded during a standard trading session in the stock market. It is the second-oldest U.S. market index after the Dow Jones Transportation Average, which was also created by Dow.

Acknowledgements

Yahoo Finance

Inspiration

Possible exploration: Stock Correlation over the last 17 years

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

Context

In the Indian Stock Market, the term "Nifty" was derived from "National" and "Fifty" as it comprised of 50 actively traded stocks in the National Stock Exchange. While initially one stock, the brand NIFTY grew to the point where it comprised of over 350 Indices as of February 28 2023, all of which serve as benchmarks for products traded on NSE.

The Nifty 50 Index consists of 50 companies spread across 13 sectors and is the largest single financial product of India. However, there are many other Indices which represent different segments and industries within the stock market and many investors use these indices to track the performance of specific sectors or market segments, and to gain exposure to different areas of the Indian economy.

Financial Investments need to be distributed across multiple investment vehicles to reduce risk. Methods to reduce risk involve diversifying one's portfolio across * various asset classes(Equity, Bonds, etc.) * sectors(IT, Pharma, etc.) * sub-groups based on parameters like Size (Large Cap, Mid Cap, Small Cap) and Geography(International Funds).

Analysis on the data of different investment vehicles could provide insights which could help an investor make informed decisions while investing and reduce risks.

Content

This dataset is divided into 3 folders. All files contain data before 7th April 2023.

Bonds and ETFs

Historical Data about two ETFs and one bond

BHARATBOND_2030: An investment option facilitated by Edelweiss Mutual Fund. Invests in bonds issues by Indian Public Sector companies.

GoldBeEs: ETF that invests in physical gold and aims to provide returns that closely correspond to the returns provided by the domestic price of gold.

SilverBeEs: ETF that invests in physical silver and aims to provide returns that closely correspond to the returns provided by the domestic price of silver.

Investment Factor Indices

Historical Data about four Indices tracking the performance of top companies based on specific parameters.

NIFTY50 Value 20: Tracks the 20 stocks which are Top ranked in Value among all the Nifty 50 stocks.

NIFTY200 Momentum 30: Tracks the 30 stocks with the highest Momentum among the Top-200 stocks.

NIFTY200 Quality 30: Tracks the 30 stocks with highest quality rating among Top-200 stocks

NIFTY Alpha Low Volatility 30: Tracks the 30 stocks with high Alpha and Low Volatility from among the Top 150 stocks

NIFTY_Indices

Contains the daily open, high, low and close of 18 NIFTY Indices across different sectors and sizes from their launch date till April 6 2023. The file CLOSE-INDICES.csv consists of the daily close prices of all 18 indices from 29 December 2006 till April 6 2023.

Acknowledgements

This data is sourced from NSE and Yahoo Finance.

Context

I created a ML app to select best algorithm using volatility indexes as well as Technology sector index.

Content

This data set includes data from 2/26/2006 until January 21, 2018.

Acknowledgements

This data set was obained directly from Yahoo's finance site. NASDAQ-100 Technology Index (NDXT) https://finance.yahoo.com/quote/%5ENDXT/history?p=^NDXT

Inspiration

My objective was to assess if volatility of the index would affect ETF performance in a significant way

The S&P 100 index is a collection of 101 constituent companies representing a diverse range of industries and sectors, including technology, finance, healthcare, and more. These companies, such as Apple, Nvidia, and Accenture, among others, have been selected based on their market capitalization, liquidity, and other factors.

The dataset constructed from Yahoo Finance data offers a comprehensive view of the daily close price variations of all 101 constituent companies over a span of 23 years, from 2000 to the present day. This dataset provides valuable insights into the performance and volatility of individual companies as well as the overall index.

Analyzing this dataset can reveal trends, patterns, and correlations within and across industries. It allows investors, analysts, and researchers to study the dynamics of the S&P 100 index and make informed decisions based on historical price movements.

By examining the historical price data, one can identify periods of growth, market fluctuations, and potential opportunities for investment. This dataset offers a wealth of information that can be leveraged for quantitative analysis, modeling, and developing trading strategies.

Overall, the S&P 100 dataset provides a captivating journey into the world of finance, offering a rich and comprehensive resource for understanding the performance of major companies across various sectors over a significant timeframe.

With this extensive dataset at your disposal, you can: - delve into long-term analysis of the performance of these prominent companies - gain valuable insights into the ebb and flow of daily price fluctuations and uncover patterns, trends, and market dynamics that have shaped the S&P 100 index over the years. - do forecasting analysis

Whether you are a seasoned investor, financial analyst, or data enthusiast, this dataset provides a valuable resource for studying the evolution of stock prices, conducting trend analysis, and making informed decisions in the ever-changing landscape of finance.

Embark on an enlightening journey of discovery as you explore the rich history and intricate price movements of the S&P 100 companies, and leverage this dataset to gain insights that can fuel your investment strategies, quantitative models, and forecasting techniques

Comprehensive daily stock market data for 21 major S&P 500 companies across diverse sectors from 2020 to 2024.

This dataset contains daily stock data for 21 prominent companies in the S&P 500 index from January 1, 2020, to December 31, 2024. Covering a range of sectors including Technology, Healthcare, Energy, Financials, Consumer, Industrials, and Cloud/Software, this dataset offers a diverse view of market trends and performance over a five-year period.

Features Include:

Date: The trading day. Open: Opening price of the stock. High: Highest price during the trading day. Low: Lowest price during the trading day. Close: Closing price of the stock. Volume: Number of shares traded. Ticker: Stock symbol representing the company.

Sectors Covered:

Technology & AI: Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), Amazon (AMZN), NVIDIA (NVDA), Taiwan Semiconductor (TSM). Healthcare: Johnson & Johnson (JNJ), UnitedHealth Group (UNH), Eli Lilly (LLY). Energy: ExxonMobil (XOM), NextEra Energy (NEE). Financial: JPMorgan Chase (JPM), Visa (V), BlackRock (BLK). Consumer: Walmart (WMT), Costco (COST), Procter & Gamble (PG). Industrial: Caterpillar (CAT), Honeywell (HON). Software/Cloud: Salesforce (CRM), ASML Holding (ASML).

This dataset is ideal for financial analysts, data scientists, and machine learning enthusiasts interested in exploring stock market trends, building predictive models, or conducting sector-based analysis over a significant time span.

Data Source: Retrieved using Yahoo Finance API, ensuring accuracy and reliability.

Usage: This dataset can be used for time-series analysis, machine learning predictions, financial modeling, and comparative studies across different sectors.

Feel free to download and explore the data, and share your findings with the community!

https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/NASDAQ_Logo.svg/1280px-NASDAQ_Logo.svg.png" alt="NASDAQ">

• The Nasdaq Stock Market ) is an American stock exchange based in New York City. It is ranked second on the list of stock exchanges by market capitalization of shares traded, behind the New York Stock Exchange.
• The exchange platform is owned by Nasdaq, Inc., which also owns the Nasdaq Nordic stock market network and several U.S. stock and options exchanges.
• "Nasdaq" was initially an acronym for the National Association of Securities Dealers Automated Quotations.
• It was founded in 1971 by the National Association of Securities Dealers (NASD), now known as the Financial Industry Regulatory Authority (FINRA).
• On February 8, 1971, the Nasdaq stock market began operations as the world's first electronic stock market.
• At first, it was merely a "quotation system" and did not provide a way to perform electronic trades.

Context

NASDAQ is one of the most popular stock exchanges in the world and the data trend determines the world economy in a way

Content

Stock prices of all NASDAQ-100 index stocks (as on Sep 2021) from 2010

Acknowledgements

Yahoo Finance API development team

Inspiration

All the Kagglers and Data Science Enthusiasts

About this Dataset

This dataset offers a comprehensive, up-to-date look at the historical stock performance of McDonald's Corporation (MCD), one of the world's most recognizable and valuable fast-food brands.

About the Company

McDonald's Corporation is an American multinational fast-food company founded in 1940. Headquartered in Chicago, Illinois, the company is the world's largest restaurant chain by revenue, with a presence in over 100 countries. McDonald's is a major component of the Dow Jones Industrial Average and the S&P 500, making its stock a key indicator for the health of the consumer discretionary sector and global consumer spending.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day.

Comprehensive History: Includes data from McDonald's early trading history to the present, offering a long-term perspective.

Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for McDonald's Corporation on the NYSE: 'MCD'.

name: The full name of the company: 'McDonald's Corporation'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases

Financial Analysis: Analyze historical price trends, volatility, and trading volume of McDonald's stock.

Machine Learning: Develop and test models for stock price prediction and time series forecasting.

Comparative Analysis: Compare McDonald's performance with other companies in the sector.

Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

About this Dataset

This dataset offers a comprehensive, up-to-date look at the historical stock performance of Netflix Inc. (NFLX), the world's leading streaming entertainment service.

About the Company

Netflix, Inc. is an American entertainment company founded in 1997. It began as a DVD-by-mail service before revolutionizing the industry with its streaming platform in 2007. Headquartered in Los Gatos, California, Netflix is a major player in content creation, production, and distribution, with a massive library of films, television series, and documentaries. As a key technology and media company, Netflix's stock performance is a significant indicator of trends in the entertainment industry and digital consumer behavior.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day.

Comprehensive History: Includes data from Netflix's early trading history to the present, offering a long-term perspective.

Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for Netflix Inc. on the NASDAQ exchange: 'NFLX'.

name: The full name of the company: 'Netflix Inc.'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases

Financial Analysis: Analyze historical price trends, volatility, and trading volume of Netflix stock.

Machine Learning: Develop and test models for stock price prediction and time series forecasting.

Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

About this Dataset

This dataset offers a comprehensive, up-to-date look at the historical stock performance of Visa Inc. (V), a global leader in digital payments. The data is provided in a clean, daily format, making it an excellent resource for financial analysis, machine learning, and time series modeling.

About the Company

Visa Inc. is an American multinational financial services corporation headquartered in San Francisco, California. The company facilitates electronic funds transfers throughout the world, most commonly through Visa-branded credit cards, debit cards, and prepaid cards. As a key player in the financial services and payment technology sectors, Visa's stock performance is a significant indicator of global consumer spending and economic trends.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day.

Comprehensive History: Includes data from Visa's early trading history to the present, offering a long-term perspective.

Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for Visa Inc. on the NYSE: 'V'.

name: The full name of the company: 'Visa Inc.'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases Financial Analysis: Analyze historical price trends, volatility, and trading volume of Visa stock.

Machine Learning: Develop and test models for stock price prediction and time series forecasting.

Comparative Analysis: Compare Visa's performance with other companies in the sector.

Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan